Dipartimento di Ingegneria civile ed ambientale
Università degli Studi di Perugia

Claudia Cerbai                            

Thesis project title: to be defined

abstract

Jessica Di Mario

Thesis project title: to be defined

abstract

Eleonora Dottorini

Thesis project title: to be defined

abstract

Livia Fabbretti                       

Thesis project title: Study and implementation of innovative methods for seismic monitoring and damage detection in strategic public buildings.

abstract

Debora Falocci                       

Thesis project title: to be defined

abstract

Gregorio Gazzetta

Thesis project title: to be defined

abstract

Pasquale Guarino

Thesis project title: to be defined

abstract

Enrique Garcia-Macias

Francesco Leopardi

Thesis project title: to be defined

abstract

Francesco Mariani

Thesis project title: Handling uncertainties in Safety Assessment of Existing Bridges - SAFER

abstract:

One of the main challenges in modern civil engineering is the multi-risk analysis of infrastructures aimed at optimizing maintenance interventions and ensuring sustainable and resilient management of transport networks. The case of bridges is particularly relevant due to the high number of structures requiring maintenance and their exposure to evolving (aging of materials) and natural (landslides, earthquakes, and floods) phenomena, prompting the recent issuance of specific regulatory measures. In this context, uncertainties regarding the environment in which the structures are located and their actual behavior play a crucial role.
Risk analyses involve the thorough evaluation of the safety of structures for which specific critical issues have been identified at a lower hierarchical level. Despite having regulatory provisions (e.g. Eurocodes or Italian NTC2018), there is a lack of a systematic approach to managing and mitigating the impact of uncertainties on the outcomes of such evaluations. Consequently, there are no significant experiences and specific indications regarding the execution of surveys, structural investigations, and instrumental monitoring aimed at improving the reliability of safety assessment, despite numerous recent contributions on the subject. In this context, digital innovation can play a decisive role in the automation and standardization of processing processes, as well as in the evaluation of evolving phenomena. Overall, it is necessary to address the problem of accurately assessing the safety of existing bridges in a severely uncertain context and employ innovative strategies, such as those based on Bayesian inference, to manage and mitigate the impact of uncertainties on safety assessment outcomes by utilizing available information based on in-situ surveys and instrumental monitoring systems.

Michele Mattiacci

Thesis project title: Advanced strategies for strain-based health monitoring of masonry structures

abstract:

Masonry structures, predominant in Europe's built heritage, face preservation challenges due to material degradation and seismic hazards. Assessing the structural integrity of masonry buildings is often a challenging task, especially in the case of historical structures. This is mainly because features such as irregular geometry, heterogeneity of masonry, material ageing, and damages suffered over the years due to unexpected loading conditions (like earthquakes and foundation settlements, to name a few) introduce uncertainties in determining their actual structural response.

Historic masonry buildings preservation throughout Europe is an immediate priority, necessitating suitable Structural Health Monitoring (SHM) solutions that should establish a connection between measured data related to the structure's in-service response and its residual load-bearing capacity and structural integrity. As of now, the widespread implementation of SHM systems in masonry structures remains limited. This is primarily due to challenges associated with off-the-shelf sensors that face issues related to scalability, durability, transmission, and high costs. Considering the above, smart materials could constitute a cutting-edge technology to overcome such limitations related to the practical implementation of SHM systems in masonry buildings. In addition to this, the analysis of data collected by sensors made from these innovative materials using AI and machine learning algorithms can enable automated earthquake-induced damage identification, quantification, and localization. The proposed research project aims to establish a novel methodology for promptly assessing the structural condition of masonry structures following a hazardous event, leveraging innovative sensing technology based on smart materials, Artificial Intelligence, and numerical modeling.

Carlos Eugenio Moureira Desousa

Thesis project title: to be defined

abstract

Martina Natali

Thesis project title: Integrated use of radar backscatter observations, machine learning and land surface modeling to improve soil and vegetation dynamics in Mediterranean agroforestry systems

abstract:

he proposed doctoral dissertation aims to enhance the understanding of soil-water-vegetation interactions in Mediterranean agroforestry systems through the integrated use of radar backscatter observations, machine learning, and land surface modeling. The research focuses on addressing the challenges posed by climate-induced water stress in rain-fed, natural ecosystems, which increasingly affects photosynthesis and evapotranspiration processes. By leveraging high-resolution, multi-frequency and multi-polarization backscatter data from different satellite platforms, and assimilating these into the Noah-Multiparameterization Land Surface Model (Noah-MP), the project seeks to improve predictions of vegetation dynamics and water stress. The methodology involves collecting remote sensing and in-situ data across selected sites in Italy, equipped with multiple instruments for measuring soil moisture, vegetation and carbon fluxes, applying machine learning to establish relationships between radar data and biophysical variables, and integrating data into the Noah-MP model through data assimilation techniques. This approach is expected to enhance the model's accuracy in simulating soil moisture, vegetation water content, and overall ecosystem responses to environmental changes. The research outcomes will offer significant implications for forestry ecosystems' services, agricultural practices and water resource management in Mediterranean environments.

Yariv Portnoy

Thesis project title: to be defined

abstract

Sara Prapotnich

Thesis project title: to be defined

abstract

Waqas Qayyum

Thesis project title: to be defined

abstract

Arash Rahimi

Thesis project title: to be defined

abstract

Mehran Shahpari

Thesis project title: to be defined

abstract

Asad Ullah

Thesis project title: to be defined

abstract

Sousa Israel (affiliation with the national doctorate "Defence from natural risks and ecological transition of buildings" with administrative headquarters at the University of Catania)

Thesis project title: to be defined

abstract